The document discusses data structures and algorithms. It defines key concepts like data items, entities, attributes, and data processing operations. It then describes common linear data structures like arrays, linked lists, stacks, and queues. Non-linear structures like trees and graphs are also covered. Common algorithms for traversing, searching, inserting, deleting, sorting, and merging data are listed. The concepts of abstract data types and algorithm analysis are briefly introduced.
Introduction: Data Types, Data structures
Types of Data Structures
Operations, ADTs
Algorithms, Comparison of Algorithms, Complexity, Time- space tradeoff.
Recursion: Introduction, format of recursive functions, recursion Vs. Iteration, examples.
Sorting: Quick sort, Merge Sort, Selection Sort
Introduction: Data Types, Data structures
Types of Data Structures
Operations, ADTs
Algorithms, Comparison of Algorithms, Complexity, Time- space tradeoff.
Recursion: Introduction, format of recursive functions, recursion Vs. Iteration, examples.
Sorting: Quick sort, Merge Sort, Selection Sort
Data Structure is the specific method for sorting out the data in a system with the goal that it could be utilized efficiently. These can implement at least one specific abstract data types (ADT), which indicate the operations that can be performed on the data structure and the computational unpredictability of those operations. Copy the link given below and paste it in new browser window to get more information on Data Structure & Algorithms:- www.transtutors.com/homework-help/computer-science/data-structure-and-algorithms.aspx
Data may be organized in many different ways; the logical or mathematical model of a particular organization of data is called "Data Structure". The choice of a particular data model depends on two considerations:
It must be rich enough in structure to reflect the actual relationships of the data in the real world.
The structure should be simple enough that one can effectively process the data when necessary.
Data Structure Operations
The particular data structure that one chooses for a given situation depends largely on the nature of specific operations to be performed.
The following are the four major operations associated with any data structure:
i. Traversing : Accessing each record exactly once so that certain items in the record may be processed.
ii. Searching : Finding the location of the record with a given key value, or finding the locations of all records which satisfy one or more conditions.
iii. Inserting : Adding a new record to the structure.
iv. Deleting : Removing a record from the structure.
Primitive and Composite Data Types
Primitive Data Types are Basic data types of any language. In most computers these are native to the machine's hardware.
Some Primitive data types are:
Integer
Data Structure is the specific method for sorting out the data in a system with the goal that it could be utilized efficiently. These can implement at least one specific abstract data types (ADT), which indicate the operations that can be performed on the data structure and the computational unpredictability of those operations. Copy the link given below and paste it in new browser window to get more information on Data Structure & Algorithms:- www.transtutors.com/homework-help/computer-science/data-structure-and-algorithms.aspx
Data may be organized in many different ways; the logical or mathematical model of a particular organization of data is called "Data Structure". The choice of a particular data model depends on two considerations:
It must be rich enough in structure to reflect the actual relationships of the data in the real world.
The structure should be simple enough that one can effectively process the data when necessary.
Data Structure Operations
The particular data structure that one chooses for a given situation depends largely on the nature of specific operations to be performed.
The following are the four major operations associated with any data structure:
i. Traversing : Accessing each record exactly once so that certain items in the record may be processed.
ii. Searching : Finding the location of the record with a given key value, or finding the locations of all records which satisfy one or more conditions.
iii. Inserting : Adding a new record to the structure.
iv. Deleting : Removing a record from the structure.
Primitive and Composite Data Types
Primitive Data Types are Basic data types of any language. In most computers these are native to the machine's hardware.
Some Primitive data types are:
Integer
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2. Values or sets of values
A “Data Item” refers to a single set of values
Group Items
Elementary Items
3. Collections of data are frequently organized
into a hierarchy of fields, records, and files.
An entity is something that has certain
attributes.
Entities with similar attributes form an entity
set.
Each attribute of an entity set has a range of
values.
4. Data with given attributes
Meaningful or processed data
Data Processing Information
5. The logical or mathematical model of a
particular organization is called data
structure.
OR
A data structure is the arrangement of data in
a computer’s memory (or sometimes on a
disk)
6. It should have two qualities:
1. It should be able to create the model as close
to reality as possible.
2. It should be simple enough to process the
data efficiently when needed.
7. There are two types of data:
1. Primitive: integers, real, character, Boolean.
2. Non-primitive: Linked-lists, stacks, queues,
trees & graphs
8.
9. Linear: elements form a sequence or a linear
list. Examples:Arrays, Linked Lists, Stacks,
Queues.
Non-linear: Data is not arranged in sequence.
The insertion and deletion of data is not
possible in a linear fashion. Examples:Trees &
Graphs
10. A list of a finite number “n” of similar data.
11. If elements of an array are referenced as
“1,2,3, … ,n”
The array is called “A”.
Then the elements of “A” are denoted by
subscript notation “a₁, a₂, a₃, … an
12. Or by the parenthesis notation
A(1), A(2), A(3), … , A(N)
Or by the bracket notation
A[1], A[2], A[3], … , A[N]
13. A linked list is a linear data structure where
each element is a separate object
A linked list has two parts: one is info and the
other is link part. Info part gives information
and link part is address of next node.
14. Each element has two items : the data and a
reference to next node
The last node has reference to null
The first node is called the head. It is not a
separate node but reference to first node.
If the list is empty then the head is a null
reference.
15. It is dynamic data structure.
The nodes can grow and shrink on demand.
Good for applications dealing with unknown
number of objects.
16. Tree or branched data structure consists of
sets of elements (nodes) which could be
linked to other elements.
Trees represent hierarchies, while graphs
represent more general relations such as
maps of city.
18. Every circle in a tree is called a node, and every
line an edge.
Root is the node without parent
Leaf is a node without child.
Internal nodes are neither leaf or root.
Path is called a sequence of nodes connecting
with edges.
19. Stacks also called a last in first out system.
Queue also called a first in first out system
Graphs contain a relationship between pairs
of elements which is not necessarily
hierarchical in nature.
20. 1. Traversing: Assessing each record exactly
once so that certain items in the record may
be processed. Also called visiting the record.
2. Searching: Finding the location of the record
with a given key value, or finding the
locations of all records which satisfy one or
more conditions.
21. 3. Inserting: Adding a new record to the structure.
4. Deleting: Removing a record from the structure.
5. Sorting: Arranging the records in some logical
order.
6. Merging: Combing the records in two different
sorted files into a single sorted file.
22. Data type defined in terms of operations on
it, and its implementation is hidden.
It is easier to replace the implementation and
it will not interfere with anything in the
program.
23. An ADT has 2 parts:
1. A name or type specifying a set of data
(dictionary)
2. Descriptions of all the operations (or methods)
that do things with that type (e.g., find, insert,
remove).The descriptions indicate what the
operations do, not how they do it.
Examples: Interfaces in Java
(roughly) header files and typedef in C
24.
25. An Algorithm is a well defined list of steps for
solving a particular problem.
The time and space it uses are two major
measures of the efficiency of an algorithm.
26. The complexity of an algorithm is the
function which gives the running time and/or
space in terms of the input size.
27. Refers to a choice between algorithmic
solutions of a data processing problems that
allows one to decrease the running time of an
algorithmic solution by increasing the space
to store that data and vice versa.
28. Linear search searches for a specified value in
a list by checking every element in the list.
Binary search method halves the number of
elements checked (in each iteration),
reducing the time taken to locate the given
item in the list.
29. Binary Search Linear Search
Works only on sorted items. such as
1,2,3,4,5,6 etc.
Works on sorted as well as unsorted
items.
12,4,5,3,2,1 etc.
Very efficient if the items are sorted Very efficient if the items are less
and present in the beginning of the
list. such as
Suppose your list items are :
12,3,4,5,1
and you want to search 12 number
then you get beginning in the list.
Works well with arrays and not on
linked lists.
Works with arrays and linked lists.
Number of comparisons are less More number of comparisons are
required if the items are present in
the later part of the array or its
elements are more.
Editor's Notes
Basic data types such as integer, real, character, and boolean are known as primitive data structures. These data types consist of characters that cannot be divided. Simple data types. These data structures are normally directly operated upon by machine-level instructions.
Non-primitive data structures are more complex data structures. These data structures are derived from the primitive data structures. They stress on formation of sets of homogeneous and heterogeneous data elements.
Linear arrays are called one-dimensional arrays because each element in such an array is referenced by one subscript. A two dimensional array is collection of similar data elements where each element is referenced by two subscripts. (such arrays are called matrices in mathematics, and tables in business applications.)